Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105621
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Peng, Z | - |
dc.creator | Gao, S | - |
dc.creator | Li, Z | - |
dc.creator | Xiao, B | - |
dc.creator | Qian, Y | - |
dc.date.accessioned | 2024-04-15T07:35:28Z | - |
dc.date.available | 2024-04-15T07:35:28Z | - |
dc.identifier.issn | 0890-8044 | - |
dc.identifier.uri | http://hdl.handle.net/10397/105621 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Z. Peng, S. Gao, Z. Li, B. Xiao and Y. Qian, "Vehicle Safety Improvement through Deep Learning and Mobile Sensing," in IEEE Network, vol. 32, no. 4, pp. 28-33, July/August 2018 is available at https://doi.org/10.1109/MNET.2018.1700389. | en_US |
dc.title | Vehicle safety improvement through deep learning and mobile sensing | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 28 | - |
dc.identifier.epage | 33 | - |
dc.identifier.volume | 32 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.1109/MNET.2018.1700389 | - |
dcterms.abstract | Information about vehicle safety, such as the driving safety status and the road safety index, is of great importance to protect humans and support safe driving route planning. Despite some research on driving safety analysis, the accuracy and granularity of driving safety assessment are both very limited. Also, the problem of precisely and dynamically predicting road safety throughout a city has not been sufficiently studied and remains open. With the proliferation of sensor-equipped vehicles and smart devices, a huge amount of mobile sensing data provides an opportunity to conduct vehicle safety analysis. In this article, we first discuss mobile sensing data collection in VANETs and then identify two main challenges in vehicle safety analysis in VANETs, i.e., driving safety analysis and road safety analysis. In each issue, we review and classify the state-of-the-art vehicle safety analysis techniques into different categories. For each category, a short description is given followed by a discussion of limitations. In order to improve vehicle safety, we propose a new deep learning framework (DeepRSI) to conduct real-time road safety prediction from the data mining perspective. Specifically, the proposed framework considers the spatio-temporal relationship of vehicle GPS trajectories and external environment factors. The evaluation results demonstrate the advantages of our proposed scheme over other methods by utilizing mobile sensing data collected in VANETs. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE network, July-Aug. 2018, v. 32, no. 4, p. 28-33 | - |
dcterms.isPartOf | IEEE network | - |
dcterms.issued | 2018-07 | - |
dc.identifier.scopus | 2-s2.0-85056876665 | - |
dc.identifier.eissn | 1558-156X | - |
dc.description.validate | 202402 bcch | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0899 | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; HK PolyU G-UACH | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 24423429 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Peng_Vehicle_Safety_Improvement.pdf | Pre-Published version | 1.05 MB | Adobe PDF | View/Open |
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